STIFF is a research project on enhancing biomorphic agility
of robot arms and hands through variable stiffness & elasticity. It is
funded by the 7th framework programme
of the European Union (grant agreement No: 231576).

Institutional PartnersGerman Aerospace Center (DLR), Germany:
Project coordinator.
Responsible for integrating a variable-impedance robotic system in the project.
Development of a novel EMG system for human impedance measurements.
Integration of human and robotic impedance control approaches.

Technische Universiteit Delft, Netherlands:
Responsible for modelling the human neuromuscular system from muscle to
joint level. Developent of time varying system identification and
parameter estimation techniques to quantify the model parameters from
recorded data using haptic manipulators.

IDSIA, Switzerland:
Responsible for learning high-level task-specific controllers based on
reinforcement signals for the flexible variable-impedance robot arm
developed by DLR, and for inverse reinforcement learning to extract
cost functions in collaboration with UEDIN.

University of Edinburgh, United Kingdom:
Responsible for the development of 'Optimal Feedback Control'
based closed loop control paradigms, specifically tailored to redundant
and variable impedance actuators. Developing
methods to extract cost functions and comparing control policies
to evaluate improvement in performance when modulating impedance
optimally.

Université Paris Descartes - CNRS, France:
Responsible for studies of impedance control in humans,
using a variety of techniques including direct physiologicial
measurements (EMG, H-reflex), mathematical modeling and robotic
simulation. The main emphasis is 1) to suggest
biologically-inspired strategies to be applied to robotics
control and 2) to use analogies with robotic devices to better
understand human behaviour in terms of impedance.

Many industrial robots are much stronger than humans, but also very inflexible. For example, humans can throw objects much further and catch them much more gracefully, temporarily storing energy in elastic tendons and muscles. Such flexible actuators, however, require more sophisticated control algorithms than those used by traditional robots.

The goal of the STIFF consortium is to equip a highly biomimetic robot hand-arm system with the agility, robustness and versatility that are the hallmarks of the human motor system, by understanding and mimicking the variable stiffness paradigms that are so effectively employed by the human central nervous system. A key component of our study will be the anatomically accurate musculoskeletal modelling of the human arm and hand.

The project will develop novel methodologies to comprehend how the human arm can adapt its impedance, e.g., by changing the co-contraction level or by adapting reflex gains. The impedances of arm and hand will be investigated using powerful robot manipulators capable of imposing force perturbations. While stiffness & elasticity are currently exploited in the context of artificial laboratory tasks, we will investigate stiffness-dependent behaviour in natural tasks such as throwing a ball or inserting a peg in a hole.

Existing closed-loop system identification techniques will be extended by non-linear time-variant techniques to identify the behaviour during reaching and grasping tasks. Grasp force modulation and hand muscle activity correlations will be acquired through machine learning techniques and then transferred to the robotic system. Finally, optimization techniques gleaned and validated on the detailed biophysical model will be transferred to the variable impedance actuation of the novel biomorphic robot.

Mitrovic, D. and Klanke, S. and Vijayakumar, S.
(2011).
Learning impedance control of antagonistic systems based on stochastic optimization principles.
The International Journal of Robotics Research
30
(5),
556.
[BibTex]